System and method for assessing and validating wind turbine and wind farm performance

ABSTRACT

A method for assessing or validating wind turbine or wind farm performance produced by one or more upgrades is provided. Measurements of operating data from wind turbines in a wind farm are obtained. Baseline models of performance are generated, and each of the baseline models is developed from a different portion of the operating data. A generating step filters the wind turbines so that they are in a balanced randomized state. An optimal baseline model of performance is selected from the baseline models and the optimal baseline model includes direction. The optimal baseline model of performance and an actual performance of the wind farm or wind turbine is compared. The comparing step determines a difference between an optimal baseline model of power output and an actual power output of the wind farm/turbine. The difference is reflective of a change in the power output produced by the upgrades.

FIELD OF THE INVENTION

The present disclosure relates generally to wind turbines and wind farmsand, more particularly, to a system and method for assessing entitlementfor and validating wind farm performance in order to determineimprovements and/or underperformance, e.g. power output.

BACKGROUND OF THE INVENTION

Wind power is considered one of the cleanest, most environmentallyfriendly energy sources presently available, and wind turbines havegained increased attention in this regard. A modern wind turbinetypically includes a tower, generator, gearbox, nacelle, and one or morerotor blades. The rotor blades capture kinetic energy of wind usingknown airfoil principles. For example, rotor blades typically have thecross-sectional profile of an airfoil such that, during operation, airflows over the blade producing a pressure difference between the sides.Consequently, a lift force, which is directed from a pressure sidetowards a suction side, acts on the blade. The lift force generatestorque on the main rotor shaft, which is geared to a generator forproducing electricity.

A plurality of wind turbines are commonly used in conjunction with oneanother to generate electricity and are commonly referred to as a “windfarm.” Wind turbines on a wind farm typically include their ownmeteorological monitors that perform, for example, temperature, windspeed, wind direction, barometric pressure, and/or air densitymeasurements. In addition, a separate meteorological mast or tower (“metmast”) having higher quality meteorological instruments that can providemore accurate measurements at one point in the farm may also beprovided. The correlation of meteorological data with power outputprovides the empirical determination of a “power curve” for theindividual wind turbines.

Typically, in a wind farm, each wind turbine attempts to maximize itsown power output while maintaining its fatigue loads within desirablelimits. To this end, each turbine includes a control module, whichattempts to maximize power output of the turbine in the face of varyingwind and grid conditions, while satisfying constraints like sub-systemratings and component loads. Based on the determined maximum poweroutput, the control module controls the operation of various turbinecomponents, such as the generator/power converter, the pitch system, thebrakes, and the yaw mechanism to reach the maximum power efficiency.

Often, while maximizing the power output of a single wind turbine,neighboring turbines may be negatively impacted. For example, downwindturbines may experience large wake effects caused by an upwind turbine.Wake effects include reduction in wind speed and increased windturbulence downwind from a wind turbine typically caused by theconventional operation of upwind turbines (i.e. for maximum poweroutput). Because of these wake effects, downwind turbines receive windat a lower speed, drastically affecting their power output (as poweroutput is proportional to wind speed). Moreover, wind turbulencenegatively affects the fatigue loads placed on the downwind turbines,and thereby affects their life (as life is proportional to fatigueloads). Consequently, maximum efficiency of a few wind turbines may leadto sub-optimal power output, performance, or longevity of other windturbines in the wind farm. Thus, modern control technologies attempt tooptimize the wind farm power output rather than the power outputs ofeach individual wind turbine.

In addition, there are many products, features, and/or upgradesavailable for wind turbines and/or wind farms to increase power outputor annual energy production (AEP) of the wind farm. Once an upgrade hasbeen installed, it is advantageous to efficiently determine various windturbine performance improvement measurements to verify the benefit ofthe upgrade. For example, a typical method for assessing wind turbineperformance measurements is to baseline power against wind speed asassessed by the turbine nacelle anemometer. The nacelle anemometerapproach, however, is sometimes hindered due to imprecision of nacelleanemometer measurements and the projection of these measurements intoAEP estimates. Further, such an approach may be less preferred than useof an external met mast in front of a wind turbine, but is in widespreaduse due to the generally prohibitive cost of the met mast approach. Inaddition, even when nacelle anemometers are calibrated correctly,individual wind power curve methods are not able to discern the benefitof upgrades, such as wake minimization technologies, that can createmore wind for the farm to use. Other types of upgrades aresoftware/controls related and involve optimizing the pitch, speed, oryaw angle of a given turbine to maximize turbine and farm power. Theseupgrades also can potentially confound anemometer-based performanceassessment as controlling these parameters can change the flow of air tothe anemometer.

Beyond assessing the performance of upgrades to turbines and farms,there is a need for performance entitlement (expectation of power)measurement techniques and methods to discern if a specific wind turbineis operating normally or if there is underperformance due to a materialor software issue. Again, anemometer-based assessment techniques canhave difficulty if there are issues with the anemometer sensor itself.

BRIEF DESCRIPTION OF THE INVENTION

Aspects and advantages of the invention will be set forth in part in thefollowing description, or may be obvious from the description, or may belearned through practice of the invention.

In one aspect, the present disclosure is directed to a method forassessing or validating wind turbine or wind farm performance producedby one or more upgrades. The method includes a measuring step formeasuring operating data, via one or more sensors, from one or more windturbines of a wind farm. A generating step generates, via a processor, aplurality of baseline models of performance of the wind farm or aplurality of baseline models of performance of a wind turbine in thewind farm from at least a portion of the operating data. Each of thebaseline models of performance is developed from a different portion ofoperating data from one or more wind turbines of the wind farm. Thegenerating step includes filtering the one or more wind turbines so thatthe one or more wind turbines are in a balanced randomized state. Aselecting step selects, via the processor, an optimal baseline model ofperformance from the plurality of baseline models, wherein the optimalbaseline model includes direction. A comparing step compares, via theprocessor, the optimal baseline model of performance and an actualperformance of the wind farm or the wind turbine. The actual performanceof the wind farm or the wind turbine is determined after one or morewind turbines of the wind farm are modified by the one or more upgradesthat have been installed for the one or more wind turbines. The upgradesinclude at least one of a tip speed ratio, pitch setting, or yaw offsetsetting. Comparing the optimal baseline model of performance and theactual performance of the wind farm or wind turbine comprisesdetermining a difference between an optimal baseline model of poweroutput and an actual power output of the wind farm or wind turbine. Thedifference is reflective of a change in the power output produced by theone or more upgrades.

In another aspect, the present disclosure is directed to a method forassessing or validating wind turbine performance. A measuring stepmeasures operating data, via one or more sensors, from the wind turbine.A generating step generates, via a processor, a plurality of baselinemodels of performance of the wind turbine from at least a portion of theoperating data. Each of the baseline models of performance is developedfrom a different portion of the operating data. The generating stepincludes filtering the data so that the data is in a balanced randomizedstate. A selecting step selects, via the processor, an optimal baselinemodel of performance from the plurality of baseline models, and theoptimal baseline model includes direction. A comparing step compares,via the processor, the optimal baseline model of performance and anactual performance of the wind turbine. The baseline models include atleast one of a tip speed ratio, pitch setting, or yaw offset setting.Comparing the optimal baseline model of performance and the actualperformance of the wind turbine comprises determining a differencebetween an optimal baseline model of power output and an actual poweroutput of the wind turbine. The difference is reflective of a change inthe power output produced by the absence or presence of one or moreupgrades to the wind turbine.

In yet another aspect, the present disclosure is directed to a systemfor validating one or more wind farm performance measurements producedby one or more upgrades. The system includes a plurality of sensorsconfigured to measure operating data from one or more wind turbines in awind farm. A controller is configured to perform a plurality ofoperations or steps. A generating step generates a plurality of baselinemodels of performance of the wind farm from at least a portion of theoperating data. Each of the baseline models of performance is developedfrom a different portion of operating data from one or more windturbines of the wind farm. The generating step includes filtering theone or more turbines so that the one or more turbines are in a balancedrandomized state. A selecting step selects an optimal baseline model ofperformance from the plurality of baseline models, and the optimalbaseline model includes direction. A comparing step compares the optimalbaseline model of performance and an actual performance of the windfarm. The actual performance of the wind farm is determined after one ormore wind turbines of the wind farm are modified by the one or moreupgrades that have been installed for the one or more wind turbines ofthe wind farm. The upgrades include at least one of tip speed ratio andpitch setting. Comparing the optimal baseline model of performance andthe actual performance of the wind farm comprises determining adifference between an optimal baseline model of power output and anactual power output of the wind farm. The difference is reflective of achange in the power output produced by the one or more upgrades.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure of the present invention, including thebest mode thereof, directed to one of ordinary skill in the art, is setforth in the specification, which makes reference to the appendedfigures, in which:

FIG. 1 illustrates a perspective view of one embodiment of a wind farmaccording to the present disclosure.

FIG. 2 illustrates a perspective view of one embodiment of a windturbine according to the present disclosure.

FIG. 3 illustrates a block diagram of one embodiment of a controller ofa wind turbine and/or or wind farm according to the present disclosure.

FIG. 4 illustrates a graph of one embodiment of the percent (%)uncertainty of the AEP change in response to one or more upgrades as afunction of the number of wind turbines according to the presentdisclosure.

FIG. 5 illustrates a flow diagram of one embodiment of a method forvalidating wind farm performance measurements, according to the presentdisclosure.

FIG. 6 illustrates a graph of one embodiment of the mean power outputfor a plurality of statistical algorithms, according to the presentdisclosure.

FIG. 7 illustrates a graph of another embodiment of the mean poweroutput for a plurality of statistical algorithms using differentfeatures, according to the present disclosure.

FIG. 8 illustrates a graph of one embodiment of the power output versustime for a plurality of baseline models, according to the presentdisclosure.

FIG. 9 illustrates a graph of one embodiment of the power output versustime for an optimal baseline model, according to the present disclosure.

FIG. 10 illustrates a layout of one embodiment of a wind farmparticularly illustrating selected wind turbines of one of the baselinemodels, according to the present disclosure.

FIG. 11 illustrates a graph of one embodiment of the mean residualbetween actual and predicted power, according to the present disclosure.

FIG. 12 illustrates a graph of one embodiment of actual and predictedpower output as a function of wind sector, according to the presentdisclosure.

FIG. 13 illustrates one embodiment a plurality of graphs of long-termwind speed distribution and a reference power curve used to integratethe power change with respect to a long-term power distribution,according to the present disclosure.

FIG. 14 illustrates a flow diagram of another embodiment of a method forvalidating wind farm performance measurements, according to the presentdisclosure.

FIG. 15 illustrates the results of a gaussian process model for aturbine of interest, according to the present disclosure.

FIG. 16 is a flowchart of a method for determining the power ensemblefor a selected wind turbine, according to the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

Reference now will be made in detail to embodiments of the invention,one or more examples of which are illustrated in the drawings. Eachexample is provided by way of explanation of the invention, notlimitation of the invention. In fact, it will be apparent to thoseskilled in the art that various modifications and variations can be madein the present invention without departing from the scope or spirit ofthe invention. For instance, features illustrated or described as partof one embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that the present inventioncovers such modifications and variations as come within the scope of theappended claims and their equivalents.

Generally, the present disclosure is directed to improved systems andmethods for validating or identifying wind farm performance measurementsin a wind farm. For example, in one embodiment, the system is configuredto validate wind farm performance measurements, i.e. power output, inresponse to one or more upgrades installed within the wind farm so as tooptimize wind farm performance. Alternatively, the system may beconfigured to identify wind farm performance measurements (i.e. problemsor errors) in a wind farm, such as, for example misconfigurations,material or software failures, and/or other problematic circumstancescausing sub-par performance of the wind farm.

In various embodiments, a farm controller is configured to estimate anannual energy production (AEP) change after an upgrade is installed forone or more wind turbines (e.g. turbine(s) of interest) of the windfarm. More specifically, the farm controller is configured to receiveand store operating data collected from a plurality of wind turbines(including the turbine(s) of interest) of the wind farm measured by oneor more sensors. The farm controller then generates a plurality ofbaseline models of performance of the wind farm (e.g. power output) fromthe operating data. For example, in one embodiment, the farm controllerselects a plurality of subsets of wind turbines from the wind farm toform the baseline models that do not include the turbine(s) of interestand selects the model that achieves the best accuracy, namely theoptimal baseline model. The farm controller then compares the optimalbaseline model of performance with the actual performance of the windfarm after one or more upgrades have been installed. For example, in oneembodiment, the controller compares the optimal baseline model of poweroutput with the actual power output and determines the associateduncertainty of the power output change. It should be understood that theupgrades may include any suitable upgrade now known or later developedin the art, including but not limited to rotor blade chord extensions,software upgrades, controls upgrades, hardware upgrades, wake controls,aerodynamic upgrades, blade tip extensions, vortex generators, winglets,or similar. Accordingly, the farm controller is configured to validatewind farm performance improvements (such as improvements in wind farmpower) that occur in response to at least one upgrade being installed.

The present disclosure has many advantages not present in the prior art.For example, the present disclosure leverages and fuses accurateavailable sensor data using machine learning algorithms. That is, themore relevant, good quality sensors used, and the more data pooled inlike conditions, the lower the predictive error of the optimal baselinemodel will be. Thus, accuracy of wind farm performance improvementmeasurements may be improved and associated costs and times may bereduced.

Referring now to the drawings, FIG. 1 illustrates an exemplaryembodiment of a wind farm 100 containing a plurality of wind turbines102 according to aspects of the present disclosure. The wind turbines102 may be arranged in any suitable fashion. By way of example, the windturbines 102 may be arranged in an array of rows and columns, in asingle row, in a random arrangement or in environmentally advantageouslocations. Further, FIG. 10 illustrates an example layout of oneembodiment of the wind farm 100. Typically, wind turbine arrangement ina wind farm is determined based on numerous optimization algorithms suchthat AEP is maximized for a corresponding site wind climate. It shouldbe understood that any wind turbine arrangement may be implemented, suchas on uneven land, without departing from the scope of the presentdisclosure.

In addition, it should be understood that the wind turbines 102 of thewind farm 100 may have any suitable configuration, such as for example,as shown in FIG. 2. As shown, the wind turbine 102 includes a tower 114extending from a support surface, a nacelle 116 mounted atop the tower114, and a rotor 118 coupled to the nacelle 116. The rotor includes arotatable hub 120 having a plurality of rotor blades 112 mountedthereon, which is, in turn, connected to a main rotor shaft that iscoupled to the generator housed within the nacelle 116 (not shown).Thus, the generator produces electrical power from the rotational energygenerated by the rotor 118. It should be appreciated that the windturbine 102 of FIG. 2 is provided for illustrative purposes only. Thus,one of ordinary skill in the art should understand that the invention isnot limited to any particular type of wind turbine configuration.

As shown generally in the figures, each wind turbine 102 of the windfarm 100 may also include a turbine controller 104 communicativelycoupled to a farm controller 108. Moreover, in one embodiment, the farmcontroller 108 may be coupled to the turbine controllers 104 through anetwork 110 to facilitate communication between the various wind farmcomponents. The wind turbines 102 may also include one or more sensors105 configured to monitor various operating, wind, and/or loadingconditions of the wind turbine 102. For instance, the one or moresensors may include blade sensors for monitoring the rotor blades 112 orpitch settings of the rotor blades; generator sensors for monitoringgenerator loads, torque, speed, acceleration and/or the power output ofthe generator; wind sensors for monitoring the one or more windconditions; and/or shaft sensors for measuring loads of the rotor shaftand/or the rotational speed of the rotor shaft. Additionally, the windturbine 102 may include one or more tower sensors for measuring theloads transmitted through the tower 114 and/or the acceleration of thetower 114. In various embodiments, the sensors may be any one of orcombination of the following: accelerometers, pressure sensors, angle ofattack sensors, vibration sensors, Miniature Inertial Measurement Units(MIMUs), camera systems, fiber optic systems, anemometers, wind vanes,Sonic Detection and Ranging (SODAR) sensors, infrared lasers, LightDetecting and Ranging (LIDAR) sensors, radiometers, pitot tubes,rawinsondes, other optical sensors, and/or any other suitable sensors.

Referring now to FIG. 3, there is illustrated a block diagram of oneembodiment of suitable components that may be included within the farmcontroller 108 and/or the turbine controller(s) 104 in accordance withaspects of the present disclosure. As shown, the controller 108 mayinclude one or more processor(s) 150 and associated memory device(s) 152configured to perform a variety of computer-implemented functions (e.g.,performing the methods, steps, calculations and the like and storingrelevant data as disclosed herein). Additionally, the controller 108 mayalso include a communications module 154 to facilitate communicationsbetween the controller 108 and the various components of the windturbine 102. Further, the communications module 154 may include a sensorinterface 156 (e.g., one or more analog-to-digital converters) to permitsignals transmitted from one or more sensors 103, 105, 107, 109 (such asthe sensors described herein) to be converted into signals that can beunderstood and processed by the processors 150. It should be appreciatedthat the sensors 103, 105, 107, 109 may be communicatively coupled tothe communications module 154 using any suitable means. For example, asshown, the sensors 103, 105, 107, 109 are coupled to the sensorinterface 156 via a wired connection. However, in other embodiments, thesensors 103, 105, 107, 109 may be coupled to the sensor interface 156via a wireless connection, such as by using any suitable wirelesscommunications protocol known in the art.

As used herein, the term “processor” refers not only to integratedcircuits referred to in the art as being included in a computer, butalso refers to a controller, a microcontroller, a microcomputer, aprogrammable logic controller (PLC), an application specific integratedcircuit, and other programmable circuits. Additionally, the memorydevice(s) 152 may generally comprise memory element(s) including, butnot limited to, computer readable medium (e.g., random access memory(RAM)), computer readable non-volatile medium (e.g., a flash memory), afloppy disk, a compact disc-read only memory (CD-ROM), a magneto-opticaldisk (MOD), a digital versatile disc (DVD) and/or other suitable memoryelements. Such memory device(s) 152 may generally be configured to storesuitable computer-readable instructions that, when implemented by theprocessor(s) 150, configure the controller 108 to perform variousfunctions as described herein. Moreover, the network 110 that couplesthe farm controller 108, the turbine controllers 104, and the windsensors 106 in the wind farm 100 may include any known communicationnetwork such as a wired or wireless network, optical networks, and thelike. In addition, the network 110 may be connected in any knowntopology, such as a ring, a bus, or hub, and may have any knowncontention resolution protocol without departing from the art. Thus, thenetwork 110 is configured to provide data communication between theturbine controller(s) 104 and the farm controller 108 in near real time.

In addition, the farm controller 108 is configured to obtain or collectdata from the sensors 103, 105, 107, 109 and/or other data sources suchas turbine empirical models. Employing these inputs, the farm controller108 is configured to determine one or more baseline models ofperformance of the wind farm 100 that can be used to validate farm-levelperformance for the wind farm 100 (e.g. farm-level power output). Morespecifically, the farm controller 108 baselines wind turbine performanceusing multi-feature estimation that normalizes AEP uncertainty estimatesand does not rely solely on nacelle anemometer estimates or expensiveadditional sensors. In one embodiment, for example, the presentdisclosure provides a unified paradigm which leverages and fuses thebest sensors available using machine learning algorithms. Thus, the farmcontroller 108 is configured to leverage big data and numerous sensorsto improve accuracy of estimates regardless of the machine-learningmodel used. For example, as shown in FIG. 4, as the number of windturbines 102 used to collect operating data increases, the percent (%)uncertainty decreases. In addition, as shown by the varying line types,as the time period for collecting data increases (e.g. from minutes toyears), the % uncertainty decreases. Thus, the more relevant, goodquality sensors used, and the more data pooled in like conditions thelower the predictive error of the model will be.

Referring now to FIG. 5, a simplified flow chart illustrating oneembodiment of a method 200 for validating performance improvementmeasurements of the wind farm 100 according to the present disclosure isillustrated. As shown at 202, the farm controller 108 may include aglobal repository configured to collect and store the operating datacollected from the sensors 103, 105, 107, 109. It should be understoodthat the wind turbine operating data may include any relevant operatingdata concerning the wind turbines 102 including but not limited to apitch angle, generator speed, power output, torque output, air density,temperature, pressure, wind speed, wind peaks, wind turbulence, windshear, wind direction, tip speed ratio, or similar.

At 204, the operating data may be filtered to provide filtered data 206.For example, in one embodiment, the data may be filtered for a varietyof reasons including but not limited to: when one of the wind turbines102 is not fully operational during a certain time interval (e.g. a10-minute interval), when curtailment is detected, and/or when datapoints have very low wind speeds (e.g. less than 2 meters/second). Itshould be understood that the data may be filtered using any othersuitable filter parameters in addition to those specifically describedherein. Operating data is filtered such that turbines are balanced instate in a randomized way. This may be done through utilizing athreshold on the standard deviation of the mean state turbine statethrough some historical window. For example, turbine state in thiscontext refers to, at a minimum tip speed ratio and/or pitch offsetsettings of the control system, but could be extended to include anyturbine configuration parameter leading to optimization of performance.This ensures there is toggling in the turbine state over the period ofinterest. This measure can be combined with a metric relating to entropyof turbine state to ensure that all turbines are not changing statetogether. Any method that ensures turbine state is randomized willsuffice. Other filters can include filtering of periods when turbinesare curtailed, or periods when some minimum number of turbines are notoperational.

At 208, the data may be optionally synchronized or harmonized so as toestablish consistency among the data collected from multiple sources.Further, at 210, the farm controller can be configured to impute datawhen missing values exist in the historical data. For example, theinitial filtering described above may create gaps in the operating data.Since various embodiments of the present disclosure require estimatingthe power from one or more wind turbine(s) of interest and usinginformation from neighboring turbines, the absence of measurements at agiven time interval requires that the corresponding data record becompletely removed, thereby leading to a significant loss of data. Thus,the farm controller 108 is configured to impute the missing data, i.e.substitute missing values with estimated values to obtain a morecomplete dataset for modeling and analysis. It should be understood thatany imputation methods known in the art may be used, including choosingto not impute but rather use the average of non-missing values as thefigure of merit. For example, in one embodiment, the missing data may beimputed using the k-nearest neighbor algorithm. Such an algorithmreplaces missing data of a given turbine with a weighted mean ofmeasurements from its k nearest-neighbors. In certain embodiments, theweights are inversely proportional to its Euclidean distance from theneighboring turbines.

In additional embodiments (e.g. when using wind speed data fromneighboring turbines), linearization of the operating data may be usefulprior to incorporation into the power model. For example, as shown at212, a Bayesian power curve methodology may be utilized to linearize theoperating data to produce power estimates for input into modelingalgorithms. At 214, linear as well as non-linear data are integrated toprovide integrated data 216.

At 218, one or more statistical models are built to estimate aperformance measurement improvement (e.g. power output) from anindividual wind turbine 102. For example, in a particular embodiment,stepwise linear regression may be utilized to estimate power output froman individual wind turbine 102. Generally, stepwise linear regressionadds or removes features one at a time in an attempt to get the bestregression model without over fitting. Further, stepwise regressiontypically has two variants, including forward and backward regression,both of which are within the scope and spirit of the invention. Forexample, forward stepwise regression is a step-by-step process ofbuilding a model by successive addition of predictor variables. At eachstep, models with and without a potential predictor variable arecompared, and the larger model is accepted only if it leads to asignificantly better fit to the data. Alternatively, backward stepwiseregression starts with a model with all predictors and removes termsthat are not statistically significant in terms of modeling a responsevariable.

Another statistical method that may be used to validate performancemeasurement improvement (e.g. power output) of the wind farm 100 is aleast absolute shrinkage and selection operator (LASSO) algorithm.Generally, a LASSO algorithm minimizes the residual sum of squaressubject to a constraint that the sum of the absolute value of thecoefficients is smaller than a constant. Still another statisticalalgorithm that may be used to validate the performance measurementimprovements from the wind farm 100 is a M5 Prime (M5P) algorithm, whichis a tree-based regression algorithm that is effective in many domains.For example, whereas stepwise linear regression produces a single globallinear model for the data, tree based regression algorithms performlogical tests on features to form a tree structure. Generally, the M5Palgorithm utilizes a linear regression model at each node of the tree,providing more specialized models. A machine learning model thatnecessarily includes direction is used along with to mean of the powerensemble group to determine entitlement (i.e., expectation of power).This can be considered an improvement over previous methods that filterdata to specific direction sectors (which then form separate models foreach sector).

Other machine learning methods shown fruitful in determining performanceentitlement models include Gaussian Process Models, Random ForestModels, and Support Vector Machines. In one implementation a Gaussianprocess model utilized with three features: sin and cosine of farmmedian direction, and the NaN-mean of “power ensemble wind turbine”powers. The “power ensemble wind turbines” are wind turbines that areidentified as significant features in determining a turbine ofinterest's power. FIG. 15 illustrates the results of a gaussian processmodel for a turbine of interest. Power ensemble validation utilizes meanpower from key reference wind turbines to determine expectation ofpower. The power ensemble for a given wind turbine is determined by thewind turbines that are most correlated to a wind turbine of interestthat together provide the lowest uncertainty in determining the windturbine of interest's performance. Advantages of power ensemble are thatuncertainty is reduced by using power only from multiple sensors.

FIG. 16 is a flowchart of a method 1600 for determining the powerensemble for a selected wind turbine. Method 1600 leverages groups ofturbines with a balanced and randomized state to reduce uncertainty, andthis method can be applied to a single wind turbine of interest or tomultiple wind turbines. The best power ensemble is determined byleveraging NaN-mean of a control wind turbine group. A null case may beutilized to ensure no bias. The method incorporates direction byinputting farm level direction into a gaussian process model along withtip speed ratio and pitch settings. The annual energy production gain isdetermined based on exercising the gaussian process model. In step 1610,the power ensemble is determined from a correlation matrix to reduce orminimize uncertainty. For example, a synchronized wind farm level powermatrix is created from the input data (which is obtained from multiplesensors). The input data is filtered to obtain a balanced set of designof experiments states and a predetermined number of operational windturbines. The power ensemble wind turbines are then ranked by pairwisecorrelation and probability of operation. The power ensemble is thendetermined by starting with the most correlated wind turbines andallowing missing values that reduces or minimizes uncertainty.

In step 1620, a create machine learning model is created. For example,the model may be a gaussian process model, a support vector regression,a random forest, a neural network model or other machine learningtechnique. The machine learning model is created from the power ensembleNaN-mean input, direction and design of experiments state to provide anoutput of estimated power for a wind turbine of interest and uncertaintyof the estimate. In step 1630 the annual energy production gain isdetermined from the estimated wind turbine power(s), as well as theuncertainty of this value. The machine learning or gaussian processmodel allows for the creation of models that interpolate well betweenpoints and also provide a measure of uncertainty. Support vectormachines and random forests will also work in this application. The sineand cosine of direction are included as explicit features in a singlemodel. An additional advantage is provided by using the NaN-mean (i.e.,mean equally weighting all non-missing wind turbine sensor values). Thismethod is particularly applicable to single wind turbines, but could beused for multiple wind turbines or even an entire wind farm.

Referring back to FIG. 6, sample results from the statistical algorithmsused to validate power output, namely stepwise linear regression (line224), LASSO (line 226), and M5P (line 228), are compared. As shown, thestepwise algorithm 224 performs as well as the more complicated LASSO(line 226) and M5P (line 228) algorithms for a particular turbine, withsome variation. Further, as shown, the LASSO (line 226) and M5P (line228) algorithms provide similar power estimates for the wind farm 100.In some months, however, M5P (line 228) performed best, whereas in othermonths, LASSO 226 performed better. It should be understood that thevarious statistical models described herein are provided for exampleonly and any other statistical models are within the spirit and scope ofthe invention.

Referring now to FIG. 7, the root mean squared error (RMSE) of predictedpower divided by mean power produced in a given month of data forvarious models are illustrated, which can be interpreted as a measure ofprediction error normalized by output power. In certain embodiments,utilization of approximately one months' worth of training data provideseffective means of predicting a turbine's power for a 100-turbine farm,however, it should be understood that any amount of data may be used todevelop each of the models. For example, as shown in the illustratedembodiment, a wind turbine 102 trained using January data may generateapproximately 5% error on the training when all features, including theBayesian Power Curve power estimate, are included in the feature set(line 230). However, when only wind speed and power are used (line 232),the error on the training data is about 8%, moving to 15% when onlypower from other turbines is used (line 234). If only wind speed is usedin the model (line 236), the error in predicting a 10-minute intervalfrom the model against training data is over 23%. When these modelstrained in January are applied to test data in the other months of theyear the error characteristic is as shown. Thus, as shown in theillustrated embodiment, the optimal results were obtained when theonboard nacelle anemometer and the Bayesian Power Curve Estimate wereincluded as features (line 230).

Referring now to FIGS. 8 and 9, the present disclosure includescomparing and analyzing the plurality of baseline models and selectingthe model that provides the best or most accurate performance tovalidate farm power output improvement in response to an upgrade, i.e.the optimal baseline model. For example, as shown in FIG. 8, the farmcontroller 108 measures a plurality of operating data from one or moresubsets of wind turbines 102 within the wind farm 100. The controller108 can then generate a plurality of baseline models for each of thesubsets of data. In certain embodiments, the controller 108 isconfigured to validate each of the baseline models by applying new datato each model (that was not used in creating the baseline models) todetermine how well each model predicted total farm power output. Forexample, in one embodiment, a first operating data set may include afirst subset and a second subset. Thus, one of the baseline models maybe generated using the first subset and then validated using the secondsubset. For example, in a wake controls embodiment, a baseline model(i.e. where wake controls are not in effect) may be exercised againstnew data where wake controls are also not in effect such that wind farmpower output can be predicted with a small margin of error. Inalternative embodiments, the controller 108 can validate the baselinemodels based on training data alone such that verification using newdata (e.g. the second subset) is not necessary.

In another embodiment, the farm controller 108 may also eliminatebaseline models that contain one or more wind turbines that have beenmodified by one or more upgrades. In still additional embodiments, thefarm controller 108 can strategically provide or withhold (e.g. turn onand off) certain upgrades or modifications (e.g. wake controls) from oneor more wind turbines in the wind farm in order to provide a basis forassessing farm performance at a desired accuracy. Further, the extent towhich modifications are left in place or removed gives the ability totrade off validation accuracy for farm performance improvement.

Referring back to FIG. 5, once the baseline models have been built andthe optimal baseline model chosen, the AEP uncertainty evaluation andstatic optimal settings are determined in step 220 which results in AEPuncertainty values 222. Applying the trained or optimal model to thetest data, the controller 108 can determine the baseline predicted powerthat is representative of the power estimate if no upgrade has beenapplied. Further, the residual power is defined as the differencebetween the observed or actual power and the baseline predicted power,which representative of the power change due to the upgrade and which isused to evaluate the AEP change. For example, as shown in FIG. 9, actualpower 238 is compared with predicted power 242 as determined by theoptimal baseline model. In the illustrated embodiment, the optimalbaseline model is based on the predicted power 242 as determined bysensors from two of the wind turbines 102. In addition, line 240represents the estimate of total farm power when half of the windturbines 102 are used in the model to predict power. As shown, thepredicted power 242 as determined by the optimal model is similar to theactual power 238 as determined by one or more sensors. Static optimalsettings are determined for turbine performance optimization byinputting tip speed ratio and pitch settings into the machine learningmodel and querying this model with all options in order to find whichsetting performs best on average. These “optimized” settings can then beapplied to the desired wind turbine(s).

Referring now to FIG. 10, the optimal baseline model is then applied tothe wind farm 100. More specifically, as shown, the farm controller 108determines the control sensors for a given sample within the set ofcontrol turbines (as indicated by markers 252) and then applies theoptimal baseline model thereto. The farm controller 108 can then modelthe error 244 or residual between actual and predicted power todetermine the performance change/uncertainty as shown in FIG. 11. Itshould be understood that accuracy of the baseline models generallyimproves with more data although any amount of data may be used to buildthe model. In certain embodiments, the controller 108 may also determinethe mean error 246 between the actual and predicted power to determine amean uncertainty value.

In additional embodiments, as shown in FIGS. 12 and 13, the farmcontroller 108 can calculate the AEP change before and after an upgradeby integrating the power change with respect to a long-term powerdistribution 304. For example, in one embodiment, the farm controller108 transforms a pre-specified long term wind speed distribution 300based on a reference power curve 302 as shown. In addition, thecontroller 108 may determine the AEP as a function of wind sector asshown in FIG. 12. More specifically, as shown, line 248 represents AEPwith wake controls, whereas line 250 represents AEP without wakecontrols. Thus, the controller 108 may be configured to compute thebaseline models for every possible wind direction. For example, in oneembodiment, the controller 108 may determine a set of models for thewind sector having a median wind direction of 15 degrees. The farmcontroller 108 can then train the models on data where the median winddirection is 15 degrees and use the models to predict farm output whenthe wind direction is 15 degrees. In such an embodiment, there aremultiple different model sets, each with a model of farm output usingevery permutation and combination of sensor inputs. Still other winddirections or sectors could be used, for example, as shown in FIG. 12(i.e. 20-degree sectors), in which case, there would be 18 differentmodel sets, and so on.

Referring now to FIG. 14, a flow diagram of a method 1400 for validatingwind farm performance improvement measurements produced by one or moreupgrades is illustrated. At 1402, the method 1400 includes measuringoperating data, via one or more sensors, from one or more wind turbinesof a wind farm. At 1404, the method includes generating a plurality ofbaseline models of performance of the wind farm from at least a portionof the operating data, wherein each of the baseline models ofperformance is developed from a different set of operating data from oneor more wind turbines of the wind farm. The generating step 1404includes filtering the data so that the turbines are in a balancedrandomized state. Control turbines are not required to be in fixed statebut rather methods for enforcing a balanced randomized state areintroduced to enable any turbine in a whole farm design of experimentsto be considered as part of the control turbine group. At 1406, themethod includes selecting, via the processor, an optimal baseline modelof performance from the plurality of baseline models. A machine learningmodel that necessarily includes direction is used along with the mean ofthe power ensemble group to determine entitlement. This is animprovement over previous methods that filtered to specific directionsectors, forming separate models for each sector. At 1408, the methodincludes comparing the optimal baseline model of performance and anactual performance of the wind farm, wherein the actual performance ofthe wind farm is determined after the one or more upgrades areinstalled. In addition in step 1408, static optimal settings aredetermined for turbine performance optimization by inputting tip speedratio and blade pitch settings into the machine learning model andquerying this model with all options in order to find which setting(s)performs best on average. These improved settings can then be applied tothe desired wind turbines.

As mentioned, the system and method as described herein may also beconfigured to identify one or more performance measurements (e.g.errors) of the wind farm, as well as performance improvements. Asdescribed herein, the term “error” is meant to encompass its ordinarymeaning as known in the art, as well as misconfigurations, material orsoftware failures, or other problematic circumstances that may causesub-par performance of the wind farm. In still additional embodiments,an error may be occurring where the wind farm is performing belowbaseline expectations for a variety of reasons. In one implementation,for example, the information from the farm validation methodology can beused to determine when a wind turbine in the wind farm or the entirewind farm is not configured correctly or has suffered from a casualty.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they include structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

The invention claimed is:
 1. A method for assessing or validating windturbine or wind farm performance produced by one or more upgrades, themethod comprising: measuring operating data, via one or more sensors,from one or more wind turbines of a wind farm; generating, via aprocessor, a plurality of baseline models of performance of the windfarm or a plurality of baseline models of performance of a wind turbinein the wind farm from at least a portion of the operating data, whereineach of the baseline models of performance is developed from a differentportion of operating data from one or more wind turbines of the windfarm, the generating step including filtering the one or more windturbines so that the one or more wind turbines are in a balancedrandomized state; selecting, via the processor, an optimal baselinemodel of performance from the plurality of baseline models the optimalbaseline model including direction; and comparing, via the processor,the optimal baseline model of performance and an actual performance ofthe wind farm or the wind turbine, wherein the actual performance of thewind farm or the wind turbine is determined after one or more windturbines of the wind farm are modified by the one or more upgrades thathave been installed for the one or more wind turbines, the upgradesincluding at least one of a tip speed ratio, pitch setting, or yawoffset setting and wherein comparing the optimal baseline model ofperformance and the actual performance of the wind farm or wind turbinecomprises determining a difference between an optimal baseline model ofpower output and an actual power output of the wind farm or windturbine, wherein the difference is reflective of a change in the poweroutput produced by the one or more upgrades.
 2. The method of claim 1,wherein the optimal baseline model of power output comprises an annualpower output of the wind farm or single wind turbine.
 3. The method ofclaim 1, wherein generating the plurality of baseline models ofperformance of the wind farm from the operating data further comprises:selecting one or more wind turbines for each of the baseline models;selecting subsets of operating data relating to each of the selectedwind turbines; and creating each of the baseline models from the subsetsof operating data.
 4. The method of claim 3, further comprisingevaluating, via the processor, each of the baseline models ofperformance of the wind farm or a wind turbine.
 5. The method of claim4, wherein evaluating each of the baseline models further comprisescomparing each of the baseline models to additional subsets of operatingdata relating to each of the selected wind turbines for each baselinemodel, wherein the additional subsets of operating data are not used increating the baseline models.
 6. The method of claim 3, furthercomprising eliminating baseline models that contain the one or more windturbines modified by the one or more upgrades.
 7. The method of claim 1,further comprising providing or withholding the one or more upgrades toor from the wind turbines of the wind farm based upon a desiredvalidation accuracy of the wind farm performance improvementmeasurements.
 8. The method of claim 1, wherein the one or more upgradescomprise any one of or a combination of the following: rotor blade chordextensions, software upgrades, controls upgrades, hardware upgrades,wake controls, aerodynamic upgrades, blade tip extensions, vortexgenerators, winglets, turbine performance optimization controls.
 9. Themethod of claim 1, further comprising generating baseline models ofpower output for a plurality of wind directions, or the sine and cosineof median wind farm direction are utilized in a machine learningalgorithm as features for a single model.
 10. The method of claim 1,further comprising developing a long-term power distribution bytransforming a pre-specified long-term wind speed distribution based ona reference power curve.
 11. The method of claim 10, further comprisingdetermining an annual energy production (AEP) change of the wind farm inresponse to the one or more upgrades by integrating the change in poweroutput produced by the one or more upgrades with respect to thelong-term power distribution.
 12. The method of claim 1, wherein the oneor more sensors comprise any one of or a combination of the following:accelerometers, pressure sensors, angle of attack sensors, vibrationsensors, Miniature Inertial Measurement Units (MIMUs), camera systems,fiber optic systems, anemometers, wind vanes, Sonic Detection andRanging (SODAR) sensors, infrared lasers, Light Detecting and Ranging(LIDAR) sensors, radiometers, pitot tubes, or rawinsondes.
 13. A methodfor assessing or validating wind turbine performance, the methodcomprising: measuring operating data, via one or more sensors, from thewind turbine; generating, via a processor, a plurality of baselinemodels of performance of the wind turbine from at least a portion of theoperating data, wherein each of the baseline models of performance isdeveloped from a different portion of the operating data, the generatingstep including filtering the data so that the data is in a balancedrandomized state; selecting, via the processor, an optimal baselinemodel of performance from the plurality of baseline models, the optimalbaseline model including direction; and comparing, via the processor,the optimal baseline model of performance and an actual performance ofthe wind turbine, the baseline models including at least one of a tipspeed ratio, pitch setting, or yaw offset setting and wherein comparingthe optimal baseline model of performance and the actual performance ofthe wind turbine comprises determining a difference between an optimalbaseline model of power output and an actual power output of the windturbine, wherein the difference is reflective of a change in the poweroutput produced by the absence or presence of one or more upgrades. 14.The method of claim 13, wherein the optimal baseline model of poweroutput comprises an annual power output of the wind turbine.
 15. Themethod of claim 13, wherein the one or more upgrades comprise any one ofor a combination of the following: rotor blade chord extensions,software upgrades, controls upgrades, hardware upgrades, wake controls,aerodynamic upgrades, blade tip extensions, vortex generators, winglets,turbine performance optimization controls.
 16. The method of claim 13,further comprising generating baseline models of power output for aplurality of wind directions, or the sine and cosine of median wind farmdirection are utilized in a machine learning algorithm as features for asingle model.
 17. The method of claim 13, further comprising developinga long-term power distribution by transforming a pre-specified long-termwind speed distribution based on a reference power curve.
 18. The methodof claim 13, wherein the one or more sensors comprise any one of or acombination of the following: accelerometers, pressure sensors, angle ofattack sensors, vibration sensors, Miniature Inertial Measurement Units(MIMUs), camera systems, fiber optic systems, anemometers, wind vanes,Sonic Detection and Ranging (SODAR) sensors, infrared lasers, LightDetecting and Ranging (LIDAR) sensors, radiometers, pitot tubes, orrawinsondes.
 19. A system for validating one or more wind farmperformance measurements produced by one or more upgrades, the systemcomprising: a plurality of sensors configured to measure operating datafrom one or more wind turbines in a wind farm; and a controllerconfigured to perform a plurality of operations, the plurality ofoperations comprising: generating a plurality of baseline models ofperformance of the wind farm from at least a portion of the operatingdata, wherein each of the baseline models of performance is developedfrom a different portion of operating data from one or more windturbines of the wind farm, the generating step including filtering theone or more turbines so that the one or more turbines are in a balancedrandomized state, selecting an optimal baseline model of performancefrom the plurality of baseline models, the optimal baseline modelincluding direction, and comparing the optimal baseline model ofperformance and an actual performance of the wind farm, wherein theactual performance of the wind farm is determined after one or more windturbines of the wind farm are modified by the one or more upgrades thathave been installed for the one or more wind turbines of the wind farm,the upgrades including at least one of tip speed ratio and pitchsetting, and wherein comparing the optimal baseline model of performanceand the actual performance of the wind farm comprises determining adifference between an optimal baseline model of power output and anactual power output of the wind farm, wherein the difference isreflective of a change in the power output produced by the one or moreupgrades.
 20. The system of claim 19, further comprising assessingperformance of individual wind turbines or groups of wind turbines todetermine underperformance in the absence of an upgrade.